A mixed black and whitelist approach for wildlife trade regulation in China: Biodiversity conservation is made of shades of gray
Bibliographic record
Abstract
Abstract The Kunming‐Montreal Global Biodiversity Framework requires effective actions to bend the curve of biodiversity loss by 2030. Wildlife trade, a direct drive of biodiversity decline, calls for more effective regulations to both protect wildlife populations in the wild and facilitate sustainable use of wildlife resources to meet human needs. This call has become particularly urgent in light of the COVID‐19 pandemic. In 2021, China's List of State Key Protected Wild Animals , a list of fauna under the strictest protection by national legislation, has been updated in the year 2021, 32 years after its first release, increasing its coverage (from the original 13%) an 11% of species across taxa. Combined with the updated List of State Protected Terrestrial Wild Animals which covers species with lower protection priority, these two national lists already cover 77% terrestrial vertebrate species of China. Such a blacklist approach, placing threatened species under a list of legal protection, is a common practice globally in species conservation. We discussed pros and cons of this dominant strategy and further explored the potential integration with a whitelist approach, listing all wildlife and only permitting regulated uses of certain species. We propose a mixed approach combining black and whitelists at different administration levels which could perhaps be first adopted in China. This is mainly due to the fact that in addition to illegal harvesting from the wild, traded wildlife in China are mostly from captive breeding and related laundering of wild‐caught animals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".